本文核心是通过提出ASK-LLM和DENSITY两种新的数据采样方法,优化大型语言模型(LLM)预训练过程中的数据效率,以实现在减少数据的同时提高模型性能。 使用ASK-LLM 和 Flan-T5-XL 作为数据质量评分器,对 T5-Large (800M) 进行数据高效的预训练运行。与在 100% 的数据集上进行训练相比,在 60% 的原始数据集上进行...
1、Flan Finetuning Figure 2:微调数据包括 473 个数据集、146 个任务类别和 1,836 个任务总数 Table 2:在多个模型中,相对于预训练,指令微调仅花费少量计算量。 Figure 3:微调数据格式的组合 1、Finetuning Data 2、Finetuning训练过程 3、Eval 2、Scaling to 540B parameters and 1.8K tasks Figure 4:多任...
对我来说,这是我在OpenAI上设置付费帐户并切换到OpenAI API的原因。...在撰写本文时,我注意到库和文档围绕OpenAI的API展开。尽管许多示例与开源基础模型google/flan-t5-xl一起使用,但我在两者之间选择了OpenAI API。 谷歌认真起来,就没 OpenAI 什么事了!创始人亲自组队创建“杀手级”多模态 AI 模型...
gs://gresearch/causallm_icl/flan_t5dec_base gs://gresearch/causallm_icl/flan_t5decplm_base gs://gresearch/causallm_icl/flan_t5dec_large gs://gresearch/causallm_icl/flan_t5decplm_large gs://gresearch/causallm_icl/flan_t5dec_xl gs://gresearch/causallm_icl/flan_t5decplm_xl...
gs://gresearch/causallm_icl/flan_t5decplm_large gs://gresearch/causallm_icl/flan_t5dec_xl gs://gresearch/causallm_icl/flan_t5decplm_xl To switch between prefixLM and causalLM attention, set the gin variable PREFIX_ATTN=True/False. ...
gs://gresearch/causallm_icl/flan_t5dec_xl \ gs://gresearch/causallm_icl/flan_t5decplm_xl To switch between prefixLM and causalLM attention, set the gin variable PREFIX_ATTN=True/False. 54 changes: 54 additions & 0 deletions 54 gins/base_deconly.gin Original file line numberDiff line...
How to debug "HFValidationError" error when loading XL model libera826·Lastcomment1y agoby Samuel Waweru Errors from flan-t5-xxl version 1 from Example Use KC·Lastcomment1y agoby BrimStoneNuke how to improve the quality of the model?
Learn more OK, Got it.Ibrahim2002 · 9mo ago· 71 views arrow_drop_up0 Copy & Edit7 more_vert google/flan-t5-xl epoch 1NotebookInputOutputLogsComments (0)comment 0 Comments Hotness
You can run XL and smaller models on NVIDIA A100 40GB, and XXL models on NVIDIA A100 80GB. Custom components The translation exampleuses the encoder-decoder model that T5X provides as well as the dataset from the T5 library. This section shows how you can use your own dataset and a model...